Introduction
Load required R packages
Collect information about papers
- Pull down papers and citation info from Crossref
- Pull down info from Whedon API
- Combine with info from GitHub issues
- Add information from software repositories
- Clean up a bit
Tabulate number of missing values
Number of published papers per month and year
Citation distribution
Most cited papers
Citation count vs time since publication
Power law of citation count within each half year
Pre-review/review time over time
Languages
Association between number of citations and number of stars of the GitHub repo
Distribution of time between GitHub repo creation and JOSS submission
Distribution of time between JOSS acceptance and last commit
Number of authors per paper
Number of authors vs number of contributors to the GitHub repo
Number of reviewers per paper
Most active reviewers
Number of papers per editor and year
Distribution of software repo licenses
Most common GitHub repo topics
Citation analysis
- Get citing papers for each submission
- Summary statistics
- Most citing journals
Save object
Session info

Introduction

In this report, we extract information about published JOSS papers and generate graphics as well as a summary table that can be downloaded and used for further analyses.

Load required R packages

suppressPackageStartupMessages({
  library(tibble)
  library(rcrossref)
  library(dplyr)
  library(tidyr)
  library(ggplot2)
  library(lubridate)
  library(gh)
  library(purrr)
  library(jsonlite)
  library(DT)
  library(plotly)
  library(citecorp)
  library(readr)
})

## Keep track of the source of each column
source_track <- c()

## Determine whether to add a caption with today's date to the (non-interactive) plots
add_date_caption <- TRUE
if (add_date_caption) {
  dcap <- lubridate::today()
} else {
  dcap <- ""
}

## Read archived version of summary data frame, to use for filling in 
## information about software repositories (due to limit on API requests)
## Sort by the date when software repo info was last obtained
papers_archive <- readRDS(gzcon(url("https://github.com/openjournals/joss-analytics/blob/gh-pages/joss_submission_analytics.rds?raw=true"))) %>%
  dplyr::arrange(!is.na(repo_info_obtained), repo_info_obtained)

## Similarly for citation analysis, to avoid having to pull down the 
## same information multiple times
citations_archive <- readr::read_delim(
  url("https://github.com/openjournals/joss-analytics/blob/gh-pages/joss_submission_citations.tsv?raw=true"),
  col_types = cols(.default = "c"), col_names = TRUE,
  delim = "\t")

Collect information about papers

Pull down papers and citation info from Crossref

We get the information about published JOSS papers from Crossref, using the rcrossref R package. This package is also used to extract citation counts.

## Fetch JOSS papers from Crossref
## Only 1000 papers at the time can be pulled down
lim <- 1000
papers <- rcrossref::cr_works(filter = c(issn = "2475-9066"), 
                              limit = lim)$data
i <- 1
while (nrow(papers) == i * lim) {
  papers <- dplyr::bind_rows(
    papers, 
    rcrossref::cr_works(filter = c(issn = "2475-9066"), 
                        limit = lim, offset = i * lim)$data)
  i <- i + 1
}
papers <- papers %>%
  dplyr::filter(type == "journal-article") 

## A few papers don't have DOIs - generate them from the URL
noaltid <- which(is.na(papers$alternative.id))
papers$alternative.id[noaltid] <- gsub("http://dx.doi.org/", "",
                                       papers$url[noaltid])

## Get citation info from Crossref and merge with paper details
cit <- rcrossref::cr_citation_count(doi = papers$alternative.id)
papers <- papers %>% dplyr::left_join(
  cit %>% dplyr::rename(citation_count = count), 
  by = c("alternative.id" = "doi")
)

## Warning in dplyr::left_join(., cit %>% dplyr::rename(citation_count = count), : Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 741 of `x` matches multiple rows in `y`.
## ℹ Row 945 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
##   "many-to-many"` to silence this warning.

## Remove one duplicated paper
papers <- papers %>% dplyr::filter(alternative.id != "10.21105/joss.00688")

source_track <- c(source_track, 
                  structure(rep("crossref", ncol(papers)), 
                            names = colnames(papers)))

Pull down info from Whedon API

For each published paper, we use the Whedon API to get information about pre-review and review issue numbers, corresponding software repository etc.

whedon <- list()
p <- 1
a0 <- NULL
a <- jsonlite::fromJSON(
  url(paste0("https://joss.theoj.org/papers/published.json?page=", p)),
  simplifyDataFrame = FALSE
)
while (length(a) > 0 && !identical(a, a0)) {
  whedon <- c(whedon, a)
  p <- p + 1
  a0 <- a
  a <- tryCatch({
    jsonlite::fromJSON(
      url(paste0("https://joss.theoj.org/papers/published.json?page=", p)),
      simplifyDataFrame = FALSE
    )}, 
    error = function(e) return(numeric(0))
  )
}

whedon <- do.call(dplyr::bind_rows, lapply(whedon, function(w) {
  data.frame(api_title = w$title, 
             api_state = w$state,
             editor = paste(w$editor, collapse = ","),
             reviewers = paste(w$reviewers, collapse = ","),
             nbr_reviewers = length(w$reviewers),
             repo_url = w$software_repository,
             review_issue_id = sub("https://github.com/openjournals/joss-reviews/issues/", "", w$paper_review),
             doi = w$doi,
             prereview_issue_id = ifelse(!is.null(w$meta_review_issue_id),
                                         w$meta_review_issue_id, NA_integer_),
             languages = paste(w$languages, collapse = ","),
             archive_doi = w$software_archive)
}))

papers <- papers %>% dplyr::left_join(whedon, by = c("alternative.id" = "doi"))

source_track <- c(source_track, 
                  structure(rep("whedon", length(setdiff(colnames(papers),
                                                         names(source_track)))), 
                            names = setdiff(colnames(papers), names(source_track))))

Combine with info from GitHub issues

From each pre-review and review issue, we extract information about review times and assigned labels.

## Pull down info on all issues in the joss-reviews repository
issues <- gh("/repos/openjournals/joss-reviews/issues", 
             .limit = 5000, state = "all")

## From each issue, extract required information
iss <- do.call(dplyr::bind_rows, lapply(issues, function(i) {
  data.frame(title = i$title, 
             number = i$number,
             state = i$state,
             opened = i$created_at,
             closed = ifelse(!is.null(i$closed_at),
                             i$closed_at, NA_character_),
             ncomments = i$comments,
             labels = paste(setdiff(
               vapply(i$labels, getElement, 
                      name = "name", character(1L)),
               c("review", "pre-review", "query-scope", "paused")),
               collapse = ","))
}))

## Split into REVIEW, PRE-REVIEW, and other issues (the latter category 
## is discarded)
issother <- iss %>% dplyr::filter(!grepl("\\[PRE REVIEW\\]", title) & 
                                    !grepl("\\[REVIEW\\]", title))
dim(issother)

## [1] 134   7

head(issother)

##                                                                                                                        title
## 1                                                                                                                        org
## 2                                                                                                              @csoneson - I
## 3                        Full author name year, rather than last name year, appearing for references to corresponding author
## 4 Example usage: Do the authors include examples of how to use the software (ideally to solve real-world analysis problems).
## 5                                                                            JOSS REVIEW- Performance -> Reading saved sites
## 6                                                                                         ## Review checklist for @mstimberg
##   number  state               opened               closed ncomments labels
## 1   5709 closed 2023-07-31T07:23:26Z 2023-07-31T07:23:28Z         1       
## 2   5708 closed 2023-07-29T18:30:07Z 2023-07-29T18:30:09Z         1       
## 3   5317 closed 2023-03-31T05:29:46Z 2023-03-31T05:29:48Z         1       
## 4   5308 closed 2023-03-28T16:27:18Z 2023-03-28T16:27:20Z         1       
## 5   5163 closed 2023-02-17T11:48:48Z 2023-02-17T11:48:51Z         1       
## 6   5002 closed 2022-12-09T16:15:19Z 2022-12-09T16:15:21Z         1

## For REVIEW issues, generate the DOI of the paper from the issue number
getnbrzeros <- function(s) {
  paste(rep(0, 5 - nchar(s)), collapse = "")
}
issrev <- iss %>% dplyr::filter(grepl("\\[REVIEW\\]", title)) %>%
  dplyr::mutate(nbrzeros = purrr::map_chr(number, getnbrzeros)) %>%
  dplyr::mutate(alternative.id = paste0("10.21105/joss.", 
                                        nbrzeros,
                                        number)) %>%
  dplyr::select(-nbrzeros) %>% 
  dplyr::mutate(title = gsub("\\[REVIEW\\]: ", "", title)) %>%
  dplyr::rename_at(vars(-alternative.id), ~ paste0("review_", .))

## For pre-review and review issues, respectively, get the number of 
## issues closed each month, and the number of those that have the 
## 'rejected' label
review_rejected <- iss %>% 
  dplyr::filter(grepl("\\[REVIEW\\]", title)) %>% 
  dplyr::filter(!is.na(closed)) %>%
  dplyr::mutate(closedmonth = lubridate::floor_date(as.Date(closed), "month")) %>%
  dplyr::group_by(closedmonth) %>%
  dplyr::summarize(nbr_issues_closed = length(labels),
                   nbr_rejections = sum(grepl("rejected", labels))) %>%
  dplyr::mutate(itype = "review")

prereview_rejected <- iss %>% 
  dplyr::filter(grepl("\\[PRE REVIEW\\]", title)) %>% 
  dplyr::filter(!is.na(closed)) %>%
  dplyr::mutate(closedmonth = lubridate::floor_date(as.Date(closed), "month")) %>%
  dplyr::group_by(closedmonth) %>%
  dplyr::summarize(nbr_issues_closed = length(labels),
                   nbr_rejections = sum(grepl("rejected", labels))) %>%
  dplyr::mutate(itype = "pre-review")

all_rejected <- dplyr::bind_rows(review_rejected, prereview_rejected)

## For PRE-REVIEW issues, add information about the corresponding REVIEW 
## issue number
isspre <- iss %>% dplyr::filter(grepl("\\[PRE REVIEW\\]", title)) %>%
  dplyr::filter(!grepl("withdrawn", labels)) %>%
  dplyr::filter(!grepl("rejected", labels))
## Some titles have multiple pre-review issues. In these cases, keep the latest
isspre <- isspre %>% dplyr::arrange(desc(number)) %>% 
  dplyr::filter(!duplicated(title)) %>% 
  dplyr::mutate(title = gsub("\\[PRE REVIEW\\]: ", "", title)) %>%
  dplyr::rename_all(~ paste0("prerev_", .))

papers <- papers %>% dplyr::left_join(issrev, by = "alternative.id") %>% 
  dplyr::left_join(isspre, by = c("prereview_issue_id" = "prerev_number")) %>%
  dplyr::mutate(prerev_opened = as.Date(prerev_opened),
                prerev_closed = as.Date(prerev_closed),
                review_opened = as.Date(review_opened),
                review_closed = as.Date(review_closed)) %>% 
  dplyr::mutate(days_in_pre = prerev_closed - prerev_opened,
                days_in_rev = review_closed - review_opened,
                to_review = !is.na(review_opened))

source_track <- c(source_track, 
                  structure(rep("joss-github", length(setdiff(colnames(papers),
                                                              names(source_track)))), 
                            names = setdiff(colnames(papers), names(source_track))))

Add information from software repositories

## Reorder so that software repositories that were interrogated longest 
## ago are checked first
tmporder <- order(match(papers$alternative.id, papers_archive$alternative.id),
                  na.last = FALSE)
software_urls <- papers$repo_url[tmporder]
is_github <- grepl("github", software_urls)
length(is_github)

## [1] 2188

sum(is_github)

## [1] 2063

software_urls[!is_github]

##   [1] "https://gitlab.com/picos-api/picos"                                    
##   [2] "https://gitlab.com/picos-api/picos"                                    
##   [3] "https://gitlab.com/picos-api/picos"                                    
##   [4] "https://gitlab.com/picos-api/picos"                                    
##   [5] "https://gitlab.com/chaver/choco-mining"                                
##   [6] "https://c4science.ch/source/tamaas/"                                   
##   [7] "https://gitlab.awi.de/sicopolis/sicopolis"                             
##   [8] "https://doi.org/10.17605/OSF.IO/3DS6A"                                 
##   [9] "https://gitlab.com/dlr-ve/esy/sfctools/framework/"                     
##  [10] "https://gitlab.com/moerman1/fhi-cc4s"                                  
##  [11] "https://gitlab.pasteur.fr/vlegrand/ROCK"                               
##  [12] "https://gitlab.inria.fr/bramas/tbfmm"                                  
##  [13] "https://gitlab.com/mmartin-lagarde/exonoodle-exoplanets/-/tree/master/"
##  [14] "https://gitlab.com/utopia-project/utopia"                              
##  [15] "https://gitlab.com/fduchate/predihood"                                 
##  [16] "https://jugit.fz-juelich.de/compflu/swalbe.jl/"                        
##  [17] "https://gitlab.com/wpettersson/kep_solver"                             
##  [18] "https://gitlab.com/ENKI-portal/ThermoCodegen"                          
##  [19] "https://gitlab.kuleuven.be/ITSCreaLab/public-toolboxes/dyntapy"        
##  [20] "https://bitbucket.org/meg/cbcbeat"                                     
##  [21] "https://gitlab.com/dmt-development/dmt-core"                           
##  [22] "https://gitlab.com/pyFBS/pyFBS"                                        
##  [23] "https://gitlab.dune-project.org/dorie/dorie"                           
##  [24] "https://gitlab.mpikg.mpg.de/curcuraci/bmiptools"                       
##  [25] "https://git.ligo.org/asimov/asimov"                                    
##  [26] "https://gitlab.com/myqueue/myqueue"                                    
##  [27] "https://bitbucket.org/orionmhdteam/orion2_release1/src/master/"        
##  [28] "https://gitlab.com/jason-rumengan/pyarma"                              
##  [29] "https://gitlab.com/fibreglass/pivc"                                    
##  [30] "https://gite.lirmm.fr/doccy/RedOak"                                    
##  [31] "https://savannah.nongnu.org/projects/complot/"                         
##  [32] "https://gitlab.inria.fr/miet/miet"                                     
##  [33] "https://gitlab.com/cosmograil/starred"                                 
##  [34] "https://bitbucket.org/hammurabicode/hamx"                              
##  [35] "http://mutabit.com/repos.fossil/grafoscopio/"                          
##  [36] "https://gitlab.com/gdetor/genetic_alg"                                 
##  [37] "https://bitbucket.org/manuela_s/hcp/"                                  
##  [38] "https://gitlab.com/cerfacs/batman"                                     
##  [39] "https://gitlab.com/manchester_qbi/manchester_qbi_public/madym_cxx/"    
##  [40] "https://gitlab.inria.fr/bcoye/game-engine-scheduling-simulation"       
##  [41] "https://gitlab.com/utopia-project/dantro"                              
##  [42] "https://gitlab.com/culturalcartography/text2map"                       
##  [43] "https://gitlab.com/petsc/petsc"                                        
##  [44] "https://gitlab.com/libreumg/dataquier.git"                             
##  [45] "https://gitlab.com/ProjectRHEA/flowsolverrhea"                         
##  [46] "https://gitlab.com/ffaucher/hawen"                                     
##  [47] "https://bitbucket.org/cardosan/brightway2-temporalis"                  
##  [48] "https://gricad-gitlab.univ-grenoble-alpes.fr/ttk/spam/"                
##  [49] "https://gitlab.com/emd-dev/emd"                                        
##  [50] "https://bitbucket.org/berkeleylab/hardware-control/src/main/"          
##  [51] "https://framagit.org/GustaveCoste/off-product-environmental-impact/"   
##  [52] "https://bitbucket.org/rram/dvrlib/src/joss/"                           
##  [53] "https://gitlab.ethz.ch/holukas/dyco-dynamic-lag-compensation"          
##  [54] "https://gitlab.com/sissopp_developers/sissopp"                         
##  [55] "https://gitlab.com/project-dare/dare-platform"                         
##  [56] "https://bitbucket.org/clhaley/Multitaper.jl"                           
##  [57] "https://gitlab.uliege.be/smart_grids/public/gboml"                     
##  [58] "https://gitlab.com/dlr-dw/ontocode"                                    
##  [59] "https://gitlab.com/marinvaders/marinvaders"                            
##  [60] "https://gitlab.com/sails-dev/sails"                                    
##  [61] "https://bitbucket.org/bmskinner/nuclear_morphology"                    
##  [62] "https://gitlab.com/InspectorCell/inspectorcell"                        
##  [63] "https://git.rwth-aachen.de/ants/sensorlab/imea"                        
##  [64] "https://gitlab.com/jesseds/apav"                                       
##  [65] "https://gitlab.com/dlr-ve/esy/amiris/amiris"                           
##  [66] "https://plmlab.math.cnrs.fr/lmrs/statistique/smmR"                     
##  [67] "https://gitlab.com/binary_c/binary_c-python/"                          
##  [68] "https://gitlab.inria.fr/melissa/melissa"                               
##  [69] "https://gitlab.gwdg.de/mpievolbio-it/crbhits"                          
##  [70] "https://gitlab.com/remram44/taguette"                                  
##  [71] "https://bitbucket.org/mpi4py/mpi4py-fft"                               
##  [72] "https://gitlab.com/cracklet/cracklet.git"                              
##  [73] "https://bitbucket.org/sciencecapsule/sciencecapsule"                   
##  [74] "https://gitlab.kitware.com/LBM/lattice-boltzmann-solver"               
##  [75] "https://gitlab.com/QComms/cqptoolkit"                                  
##  [76] "https://gitlab.com/eidheim/Simple-Web-Server"                          
##  [77] "https://gitlab.com/fame-framework/fame-io"                             
##  [78] "https://gitlab.com/toposens/public/ros-packages"                       
##  [79] "https://gitlab.inria.fr/azais/treex"                                   
##  [80] "https://gitlab.com/pvst/asi"                                           
##  [81] "https://bitbucket.org/cdegroot/wediff"                                 
##  [82] "https://bitbucket.org/basicsums/basicsums"                             
##  [83] "https://framagit.org/GustaveCoste/eldam"                               
##  [84] "https://gitlab.com/thartwig/asloth"                                    
##  [85] "https://gitlab.com/dsbowen/conditional-inference"                      
##  [86] "https://code.usgs.gov/umesc/quant-ecology/fishstan/"                   
##  [87] "https://git.geomar.de/digital-earth/dasf/dasf-messaging-python"        
##  [88] "https://bitbucket.org/glotzer/rowan"                                   
##  [89] "https://gitlab.ifremer.fr/resourcecode/resourcecode"                   
##  [90] "https://www.idpoisson.fr/fullswof/"                                    
##  [91] "https://gitlab.com/fame-framework/fame-core"                           
##  [92] "https://gitlab.com/sigcorr/sigcorr"                                    
##  [93] "https://gitlab.com/moorepants/skijumpdesign"                           
##  [94] "https://git.iws.uni-stuttgart.de/tools/frackit"                        
##  [95] "https://gitlab.com/cosmograil/PyCS3"                                   
##  [96] "https://bitbucket.org/miketuri/perl-spice-sim-seus/"                   
##  [97] "https://gitlab.com/dlr-ve/autumn/"                                     
##  [98] "https://bitbucket.org/ocellarisproject/ocellaris"                      
##  [99] "https://gitlab.inria.fr/mosaic/bvpy"                                   
## [100] "https://bitbucket.org/berkeleylab/esdr-pygdh/"                         
## [101] "https://gitlab.com/LMSAL_HUB/aia_hub/aiapy"                            
## [102] "https://sourceforge.net/p/mcapl/mcapl_code/ci/master/tree/"            
## [103] "https://gitlab.com/davidtourigny/dynamic-fba"                          
## [104] "https://gitlab.com/ags-data-format-wg/ags-python-library"              
## [105] "https://bitbucket.org/likask/mofem-cephas"                             
## [106] "https://bitbucket.org/dolfin-adjoint/pyadjoint"                        
## [107] "https://bitbucket.org/cmutel/brightway2"                               
## [108] "https://gitlab.com/drti/basic-tools"                                   
## [109] "https://gitlab.com/materials-modeling/wulffpack"                       
## [110] "https://git.mpib-berlin.mpg.de/castellum/castellum"                    
## [111] "https://gitlab.com/permafrostnet/teaspoon"                             
## [112] "https://gitlab.com/tesch1/cppduals"                                    
## [113] "https://gitlab.com/geekysquirrel/bigx"                                 
## [114] "https://bitbucket.org/cloopsy/android/"                                
## [115] "https://bitbucket.org/dghoshal/frieda"                                 
## [116] "https://bitbucket.org/mituq/muq2.git"                                  
## [117] "https://gitlab.com/programgreg/tagginglatencyestimator"                
## [118] "https://gitlab.ruhr-uni-bochum.de/reichp2y/proppy"                     
## [119] "https://gitlab.com/datafold-dev/datafold/"                             
## [120] "https://gitlab.com/costrouc/pysrim"                                    
## [121] "https://gitlab.com/dglaeser/fieldcompare"                              
## [122] "https://gitlab.com/energyincities/besos/"                              
## [123] "https://gitlab.com/gims-developers/gims"                               
## [124] "https://gitlab.com/celliern/scikit-fdiff/"                             
## [125] "https://gitlab.com/ampere2/metalwalls"

df <- do.call(dplyr::bind_rows, lapply(software_urls[is_github], function(u) {
  u0 <- gsub("^http://", "https://", gsub("\\.git$", "", gsub("/$", "", u)))
  if (grepl("/tree/", u0)) {
    u0 <- strsplit(u0, "/tree/")[[1]][1]
  }
  if (grepl("/blob/", u0)) {
    u0 <- strsplit(u0, "/blob/")[[1]][1]
  }
  info <- try({
    gh(gsub("(https://)?(www.)?github.com/", "/repos/", u0))
  })
  languages <- try({
    gh(paste0(gsub("(https://)?(www.)?github.com/", "/repos/", u0), "/languages"), 
       .limit = 500)
  })
  topics <- try({
    gh(paste0(gsub("(https://)?(www.)?github.com/", "/repos/", u0), "/topics"), 
       .accept = "application/vnd.github.mercy-preview+json", .limit = 500)
  })
  contribs <- try({
    gh(paste0(gsub("(https://)?(www.)?github.com/", "/repos/", u0), "/contributors"), 
       .limit = 500)
  })
  if (!is(info, "try-error") && length(info) > 1) {
    if (!is(contribs, "try-error")) {
      if (length(contribs) == 0) {
        repo_nbr_contribs <- repo_nbr_contribs_2ormore <- NA_integer_
      } else {
        repo_nbr_contribs <- length(contribs)
        repo_nbr_contribs_2ormore <- sum(vapply(contribs, function(x) x$contributions >= 2, NA_integer_))
        if (is.na(repo_nbr_contribs_2ormore)) {
          print(contribs)
        }
      }
    } else {
      repo_nbr_contribs <- repo_nbr_contribs_2ormore <- NA_integer_
    }
    
    if (!is(languages, "try-error")) {
      if (length(languages) == 0) {
        repolang <- ""
      } else {
        repolang <- paste(paste(names(unlist(languages)), 
                                unlist(languages), sep = ":"), collapse = ",")
      }
    } else {
      repolang <- ""
    }
    
    if (!is(topics, "try-error")) {
      if (length(topics$names) == 0) {
        repotopics <- ""
      } else {
        repotopics <- paste(unlist(topics$names), collapse = ",")
      }
    } else {
      repotopics <- ""
    }
    
    data.frame(repo_url = u, 
               repo_created = info$created_at,
               repo_updated = info$updated_at,
               repo_pushed = info$pushed_at,
               repo_nbr_stars = info$stargazers_count,
               repo_language = ifelse(!is.null(info$language),
                                      info$language, NA_character_),
               repo_languages_bytes = repolang,
               repo_topics = repotopics,
               repo_license = ifelse(!is.null(info$license),
                                     info$license$key, NA_character_),
               repo_nbr_contribs = repo_nbr_contribs,
               repo_nbr_contribs_2ormore = repo_nbr_contribs_2ormore
    )
  } else {
    NULL
  }
})) %>%
  dplyr::mutate(repo_created = as.Date(repo_created),
                repo_updated = as.Date(repo_updated),
                repo_pushed = as.Date(repo_pushed)) %>%
  dplyr::distinct() %>%
  dplyr::mutate(repo_info_obtained = lubridate::today())
stopifnot(length(unique(df$repo_url)) == length(df$repo_url))
dim(df)

## For papers not in df (i.e., for which we didn't get a valid response
## from the GitHub API query), use information from the archived data frame
dfarchive <- papers_archive %>% 
  dplyr::select(colnames(df)[colnames(df) %in% colnames(papers_archive)]) %>%
  dplyr::filter(!(repo_url %in% df$repo_url))
df <- dplyr::bind_rows(df, dfarchive)

papers <- papers %>% dplyr::left_join(df, by = "repo_url")

source_track <- c(source_track, 
                  structure(rep("sw-github", length(setdiff(colnames(papers),
                                                            names(source_track)))), 
                            names = setdiff(colnames(papers), names(source_track))))

Clean up a bit

## Convert publication date to Date format
## Add information about the half year (H1, H2) of publication
## Count number of authors
papers <- papers %>% dplyr::select(-reference, -license, -link) %>%
  dplyr::mutate(published.date = as.Date(published.print)) %>% 
  dplyr::mutate(
    halfyear = paste0(year(published.date), 
                      ifelse(month(published.date) <= 6, "H1", "H2"))
  ) %>% dplyr::mutate(
    halfyear = factor(halfyear, 
                      levels = paste0(rep(sort(unique(year(published.date))), 
                                          each = 2), c("H1", "H2")))
  ) %>% dplyr::mutate(nbr_authors = vapply(author, function(a) nrow(a), NA_integer_))
papers <- papers %>% dplyr::distinct()

source_track <- c(source_track, 
                  structure(rep("cleanup", length(setdiff(colnames(papers),
                                                          names(source_track)))), 
                            names = setdiff(colnames(papers), names(source_track))))

Tabulate number of missing values

In some cases, fetching information from (e.g.) the GitHub API fails for a subset of the publications. There are also other reasons for missing values (for example, the earliest submissions do not have an associated pre-review issue). The table below lists the number of missing values for each of the variables in the data frame.

DT::datatable(
  data.frame(variable = colnames(papers),
             nbr_missing = colSums(is.na(papers))) %>%
    dplyr::mutate(source = source_track[variable]),
  escape = FALSE, rownames = FALSE, 
  filter = list(position = 'top', clear = FALSE),
  options = list(scrollX = TRUE)
)

Number of published papers per month and year

ggplot(papers %>% 
         dplyr::mutate(pubmonth = lubridate::floor_date(published.date, "month")) %>%
         dplyr::group_by(pubmonth) %>%
         dplyr::summarize(npub = n()), 
       aes(x = factor(pubmonth), y = npub)) + 
  geom_bar(stat = "identity") + theme_minimal() + 
  labs(x = "", y = "Number of published papers per month", caption = dcap) + 
  theme(axis.title = element_text(size = 15),
        axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))

ggplot(papers %>% 
         dplyr::mutate(pubyear = lubridate::year(published.date)) %>%
         dplyr::group_by(pubyear) %>%
         dplyr::summarize(npub = n()), 
       aes(x = factor(pubyear), y = npub)) + 
  geom_bar(stat = "identity") + theme_minimal() + 
  labs(x = "", y = "Number of published papers per year", caption = dcap) + 
  theme(axis.title = element_text(size = 15),
        axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))

The plots below illustrate the fraction of pre-review and review issues closed during each month that have the ‘rejected’ label attached.

ggplot(all_rejected, 
       aes(x = factor(closedmonth), y = nbr_rejections/nbr_issues_closed)) + 
  geom_bar(stat = "identity") + 
  theme_minimal() + 
  facet_wrap(~ itype, ncol = 1) + 
  labs(x = "Month of issue closing", y = "Fraction of issues rejected",
       caption = dcap) + 
  theme(axis.title = element_text(size = 15),
        axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))

Citation distribution

Papers with 20 or more citations are grouped in the “>=20” category.

ggplot(papers %>% 
         dplyr::mutate(citation_count = replace(citation_count,
                                                citation_count >= 20, ">=20")) %>%
         dplyr::mutate(citation_count = factor(citation_count, 
                                               levels = c(0:20, ">=20"))) %>%
         dplyr::group_by(citation_count) %>%
         dplyr::tally(),
       aes(x = citation_count, y = n)) + 
  geom_bar(stat = "identity") + 
  theme_minimal() + 
  labs(x = "Crossref citation count", y = "Number of publications", caption = dcap)

Most cited papers

The table below sorts the JOSS papers in decreasing order by the number of citations in Crossref.

DT::datatable(
  papers %>% 
    dplyr::mutate(url = paste0("<a href='", url, "' target='_blank'>", 
                               url,"</a>")) %>% 
    dplyr::arrange(desc(citation_count)) %>% 
    dplyr::select(title, url, published.date, citation_count),
  escape = FALSE,
  filter = list(position = 'top', clear = FALSE),
  options = list(scrollX = TRUE)
)

Citation count vs time since publication

plotly::ggplotly(
  ggplot(papers, aes(x = published.date, y = citation_count, label = title)) + 
    geom_point(alpha = 0.5) + theme_bw() + scale_y_sqrt() + 
    geom_smooth() + 
    labs(x = "Date of publication", y = "Crossref citation count", caption = dcap) + 
    theme(axis.title = element_text(size = 15)),
  tooltip = c("label", "x", "y")
)

## Warning: The following aesthetics were dropped during statistical transformation: label
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
##   the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
##   variable into a factor?

Power law of citation count within each half year

Here, we plot the citation count for all papers published within each half year, sorted in decreasing order.

ggplot(papers %>% dplyr::group_by(halfyear) %>% 
         dplyr::arrange(desc(citation_count)) %>%
         dplyr::mutate(idx = seq_along(citation_count)), 
       aes(x = idx, y = citation_count)) + 
  geom_point(alpha = 0.5) + 
  facet_wrap(~ halfyear, scales = "free") + 
  theme_bw() + 
  labs(x = "Index", y = "Crossref citation count", caption = dcap)

Pre-review/review time over time

In these plots we investigate whether the time a submission spends in the pre-review or review stage (or their sum) has changed over time. The blue curve corresponds to a rolling median for submissions over 120 days.

## Helper functions (modified from https://stackoverflow.com/questions/65147186/geom-smooth-with-median-instead-of-mean)
rolling_median <- function(formula, data, xwindow = 120, ...) {
  ## Get order of x-values and sort x/y
  ordr <- order(data$x)
  x <- data$x[ordr]
  y <- data$y[ordr]
  
  ## Initialize vector for smoothed y-values
  ys <- rep(NA, length(x))
  ## Calculate median y-value for each unique x-value
  for (xs in setdiff(unique(x), NA)) {
    ## Get x-values in the window, and calculate median of corresponding y
    j <- ((xs - xwindow/2) < x) & (x < (xs + xwindow/2))
    ys[x == xs] <- median(y[j], na.rm = TRUE)
  }
  y <- ys
  structure(list(x = x, y = y, f = approxfun(x, y)), class = "rollmed")
}

predict.rollmed <- function(mod, newdata, ...) {
  setNames(mod$f(newdata$x), newdata$x)
}

ggplot(papers, aes(x = prerev_opened, y = as.numeric(days_in_pre))) + 
  geom_point() + 
  geom_smooth(formula = y ~ x, method = "rolling_median", 
              se = FALSE, method.args = list(xwindow = 120)) + 
  theme_bw() + 
  labs(x = "Date of pre-review opening", y = "Number of days in pre-review", 
       caption = dcap) + 
  theme(axis.title = element_text(size = 15))

ggplot(papers, aes(x = review_opened, y = as.numeric(days_in_rev))) + 
  geom_point() +
  geom_smooth(formula = y ~ x, method = "rolling_median", 
              se = FALSE, method.args = list(xwindow = 120)) +
  theme_bw() + 
  labs(x = "Date of review opening", y = "Number of days in review", 
       caption = dcap) + 
  theme(axis.title = element_text(size = 15))

ggplot(papers, aes(x = prerev_opened, 
                   y = as.numeric(days_in_pre) + as.numeric(days_in_rev))) + 
  geom_point() +
  geom_smooth(formula = y ~ x, method = "rolling_median", 
              se = FALSE, method.args = list(xwindow = 120)) +
  theme_bw() + 
  labs(x = "Date of pre-review opening", y = "Number of days in pre-review + review", 
       caption = dcap) + 
  theme(axis.title = element_text(size = 15))

Languages

Next, we consider the languages used by the submissions, both as reported by Whedon and based on the information encoded in available GitHub repositories (for the latter, we also record the number of bytes of code written in each language). Note that a given submission can use multiple languages.

## Language information from Whedon
sspl <- strsplit(papers$languages, ",")
all_languages <- unique(unlist(sspl))
langs <- do.call(dplyr::bind_rows, lapply(all_languages, function(l) {
  data.frame(language = l,
             nbr_submissions_Whedon = sum(vapply(sspl, function(v) l %in% v, 0)))
}))

## Language information from GitHub software repos
a <- lapply(strsplit(papers$repo_languages_bytes, ","), function(w) strsplit(w, ":"))
a <- a[sapply(a, length) > 0]
langbytes <- as.data.frame(t(as.data.frame(a))) %>% 
  setNames(c("language", "bytes")) %>%
  dplyr::mutate(bytes = as.numeric(bytes)) %>%
  dplyr::filter(!is.na(language)) %>%
  dplyr::group_by(language) %>%
  dplyr::summarize(nbr_bytes_GitHub = sum(bytes),
                   nbr_repos_GitHub = length(bytes)) %>%
  dplyr::arrange(desc(nbr_bytes_GitHub))

langs <- dplyr::full_join(langs, langbytes, by = "language")

ggplot(langs %>% dplyr::arrange(desc(nbr_submissions_Whedon)) %>%
         dplyr::filter(nbr_submissions_Whedon > 10) %>%
         dplyr::mutate(language = factor(language, levels = language)),
       aes(x = language, y = nbr_submissions_Whedon)) + 
  geom_bar(stat = "identity") + 
  theme_bw() + 
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) + 
  labs(x = "", y = "Number of submissions", caption = dcap) + 
  theme(axis.title = element_text(size = 15))

DT::datatable(
  langs %>% dplyr::arrange(desc(nbr_bytes_GitHub)),
  escape = FALSE,
  filter = list(position = 'top', clear = FALSE),
  options = list(scrollX = TRUE)
)

ggplot(langs, aes(x = nbr_repos_GitHub, y = nbr_bytes_GitHub)) + 
  geom_point() + scale_x_log10() + scale_y_log10() + geom_smooth() + 
  theme_bw() + 
  labs(x = "Number of repos using the language",
       y = "Total number of bytes of code\nwritten in the language", 
       caption = dcap) + 
  theme(axis.title = element_text(size = 15))

Association between number of citations and number of stars of the GitHub repo

ggplotly(
  ggplot(papers, aes(x = citation_count, y = repo_nbr_stars,
                     label = title)) + 
    geom_point(alpha = 0.5) + scale_x_sqrt() + scale_y_sqrt() + 
    theme_bw() + 
    labs(x = "Crossref citation count", y = "Number of stars, GitHub repo", 
         caption = dcap) + 
    theme(axis.title = element_text(size = 15)),
  tooltip = c("label", "x", "y")
)

Distribution of time between GitHub repo creation and JOSS submission

ggplot(papers, aes(x = as.numeric(prerev_opened - repo_created))) +
  geom_histogram(bins = 50) + 
  theme_bw() + 
  labs(x = "Time (days) from repo creation to JOSS pre-review start", 
       caption = dcap) + 
  theme(axis.title = element_text(size = 15))

Distribution of time between JOSS acceptance and last commit

ggplot(papers, aes(x = as.numeric(repo_pushed - review_closed))) +
  geom_histogram(bins = 50) + 
  theme_bw() + 
  labs(x = "Time (days) from closure of JOSS review to most recent commit in repo",
       caption = dcap) + 
  theme(axis.title = element_text(size = 15)) + 
  facet_wrap(~ year(published.date), scales = "free_y")

Number of authors per paper

List the papers with the largest number of authors, and display the distribution of the number of authors per paper, for papers with at most 20 authors.

## Papers with largest number of authors
papers %>% dplyr::arrange(desc(nbr_authors)) %>% 
  dplyr::select(title, published.date, url, nbr_authors) %>%
  as.data.frame() %>% head(10)

##                                                                                                                          title
## 1                                                                                    SunPy: A Python package for Solar Physics
## 2                                                        ENZO: An Adaptive Mesh Refinement Code for Astrophysics (Version 2.6)
## 3  The Pencil Code, a modular MPI code for partial differential equations and particles: multipurpose and multiuser-maintained
## 4                                                     GRChombo: An adaptable numerical relativity code for fundamental physics
## 5                                                                                       PyBIDS: Python tools for BIDS datasets
## 6                                       DataLad: distributed system for joint management of code, data, and their relationship
## 7                                                                            Chaste: Cancer, Heart and Soft Tissue Environment
## 8                                                                           spam: Software for Practical Analysis of Materials
## 9                                                       SNEWPY: A Data Pipeline from Supernova Simulations to Neutrino Signals
## 10                                                                                       VIVO: a system for research discovery
##    published.date                                   url nbr_authors
## 1      2020-02-14 http://dx.doi.org/10.21105/joss.01832         124
## 2      2019-10-03 http://dx.doi.org/10.21105/joss.01636          55
## 3      2021-02-21 http://dx.doi.org/10.21105/joss.02807          38
## 4      2021-12-10 http://dx.doi.org/10.21105/joss.03703          32
## 5      2019-08-12 http://dx.doi.org/10.21105/joss.01294          31
## 6      2021-07-01 http://dx.doi.org/10.21105/joss.03262          31
## 7      2020-03-13 http://dx.doi.org/10.21105/joss.01848          29
## 8      2020-07-13 http://dx.doi.org/10.21105/joss.02286          27
## 9      2021-11-27 http://dx.doi.org/10.21105/joss.03772          26
## 10     2019-07-26 http://dx.doi.org/10.21105/joss.01182          25

nbins <- max(papers$nbr_authors[papers$nbr_authors <= 20])
ggplot(papers %>% dplyr::filter(nbr_authors <= 20),
  aes(x = nbr_authors)) + 
  geom_histogram(bins = nbins, fill = "lightgrey", color = "grey50") + 
  theme_bw() + 
  facet_wrap(~ year(published.date), scales = "free_y") + 
  theme(axis.title = element_text(size = 15)) + 
  labs(x = "Number of authors",
       y = "Number of publications with\na given number of authors", 
       caption = dcap)

ggplot(papers %>% 
         dplyr::mutate(nbr_authors = replace(nbr_authors, nbr_authors > 5, ">5")) %>%
         dplyr::mutate(nbr_authors = factor(nbr_authors, levels = c("1", "2", "3", 
                                                                    "4", "5", ">5"))) %>%
         dplyr::mutate(year = year(published.date)) %>%
         dplyr::mutate(year = factor(year)) %>%
         dplyr::group_by(year, nbr_authors, .drop = FALSE) %>%
         dplyr::summarize(n = n()) %>%
         dplyr::mutate(freq = n/sum(n)) %>%
         dplyr::mutate(year = as.integer(as.character(year))), 
       aes(x = year, y = freq, fill = nbr_authors)) + geom_area() + 
  theme_minimal() + 
  scale_fill_brewer(palette = "Set1", name = "Number of\nauthors", 
                    na.value = "grey") + 
  theme(axis.title = element_text(size = 15)) + 
  labs(x = "Year", y = "Fraction of submissions", caption = dcap)

Number of authors vs number of contributors to the GitHub repo

Note that points are slightly jittered to reduce the overlap.

plotly::ggplotly(
  ggplot(papers, aes(x = nbr_authors, y = repo_nbr_contribs_2ormore, label = title)) + 
    geom_abline(slope = 1, intercept = 0) + 
    geom_jitter(width = 0.05, height = 0.05, alpha = 0.5) + 
    # geom_point(alpha = 0.5) + 
    theme_bw() + 
    scale_x_sqrt() + scale_y_sqrt() + 
    labs(x = "Number of authors", 
         y = "Number of contributors\nwith at least 2 commits", 
         caption = dcap) + 
    theme(axis.title = element_text(size = 15)),
  tooltip = c("label", "x", "y")
)

Number of reviewers per paper

Submissions associated with rOpenSci and pyOpenSci are not considered here, since they are not explicitly reviewed at JOSS.

ggplot(papers %>%
         dplyr::filter(!grepl("rOpenSci|pyOpenSci", prerev_labels)) %>%
         dplyr::mutate(year = year(published.date)),
       aes(x = nbr_reviewers)) + geom_bar() + 
  facet_wrap(~ year) + theme_bw() + 
  labs(x = "Number of reviewers", y = "Number of submissions", caption = dcap)

Most active reviewers